Comparing Interval Restricted Estimators in Hedonic Pricing

In hedonic pricing models there is often prior knowledge available which has the form of interval constraints on the unknown coefficients. These are stemming for example from considerations of submarkets for the characteristics involved. In this article we briefly discuss some well known estimators that allow for incorporation of this knowledge. Additionally we introduce two new promising approaches for the same purpose: a modified Bayesian approach and a method applying fuzzy interval constraints. Using data on housing prices we present the results of a Monte Carlo experiment in which these estimators are compared. It turns out that constrained estimation is promising especially in the situation of high multicollinearity and moderate R2 which is typical for hedonic pricing models. We illustrate that estimates and confidence intervals for the unknown coefficients can be improved substantially compared with the conventional unrestricted estimation.